For generating supervisory inputs, the central bank has floated an expression of interest (EoI) for engaging consultants in the use of Advanced Analytics, AI and ML
Extensive usage of advanced analytics, artificial intelligence and machine learning (AI and ML) is to be made by the Reserve Bank of India (RBI) for analysing its huge database and improve regulatory supervision on banks and NBFCs.
The central bank is also looking to hire external experts for this purpose.
While the RBI is already using AI and ML in supervisory processes, it now intends to upscale it to ensure that the benefits of advanced analytics can accrue to the Department of Supervision in the central bank.
For supervisory examinations, the department has been developing and using linear and a few machine-learnt models.
The supervisory jurisdictions of the RBI extends over banks, urban cooperative banks (UCB), NBFCs, payment banks, small finance banks, local area banks, credit information companies and select all India financial institutions.
With the help of on-site inspections and off-site monitoring, it undertakes continuous supervision of such entities.
For generating supervisory inputs, the central bank has floated an expression of interest (EoI) for engaging consultants in the use of Advanced Analytics, AI and ML.
“Taking note of the global supervisory applications of AI and ML applications, this Project has been conceived for use of Advance Analytics and AI/ML to expand analysis of huge data repository with RBI and externally, through the engagement of external experts, which is expected to greatly enhance the effectiveness and sharpness of supervision,” it said.
The selected consultant will be required to explore and profile data with a supervisory focus, among other things.
The EoI said that the objective is to enhance the data-driven surveillance capabilities of the Reserve Bank.
For assisting supervisory and regulatory activities, regulatory and supervisory authorities, across the world, are using machine learning techniques (commonly referred to as ‘Supertech’ and ‘regtech’), it added.
Most of these techniques are still exploratory; however, they are rapidly gaining popularity and scale.
AI and ML technologies, on the data collection side, are used for real-time data reporting, effective data management and dissemination.
For data analytics, these are being used for monitoring supervised firm-specific risks, including liquidity risks, market risks, credit exposures and concentration risks; misconduct analysis; and mis-selling of products.
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